This document discusses various image processing techniques in Matlab including edge detection, the radon transform, inverse radon transform, and marker-controlled watershed segmentation. It provides code examples for detecting edges using the edge function, computing projections of an image using the radon transform, reconstructing images from projections using the inverse radon transform, and segmenting touching objects using marker-controlled watershed segmentation.

1. Matlab:Feature Extraction Using Segmentation and Edge Detection

2. Detecting Edges Using the edge Function In an image, an edge is a curve that follows a path of rapid change in image intensity.

4. imshow(I);

5. BW1 = edge(I,'sobel');

6. BW2 = edge(I,'canny');

7. figure, imshow(BW1) ;

9. imshow(I);

10. BW1 = edge(I,'sobel');

11. BW2 = edge(I,'canny');

12. figure, imshow(BW1) ;

14. Radon Transform The radon function computes projections of an image matrix along specified directions.

15. Radon Transform >> I=zeros(100,100); >> I(40:60, 40:60)=1; >> imshow(I); >> [R,xp] = radon(I,0); >> figure,plot(xp,R);

16. Radon Transform >> I=zeros(100,100); >> I(40:60, 40:60)=1; >> imshow(I); >> [R,xp] = radon(I,45); >> figure,plot(xp,R);

17. Inverse Radon Transform The iradon function inverts the Radon transform and can therefore be used to reconstruct images. iradon reconstructs an image from parallel-beam projections. In parallel-beam geometry, each projection is formed by combining a set of line integrals through an image at a specific angle.

18. Inverse Radon Transform P = phantom(def, n) generates an image of a head phantom that can be used to test the numerical accuracy of radon and iradon or other two-dimensional reconstruction algorithms.

19. Inverse Radon Transform >> P=phantom(256); >> imshow(P) >> theta1 = 0:10:170; [R1,xp] = radon(P,theta1); theta2 = 0:5:175; [R2,xp] = radon(P,theta2); theta3 = 0:2:178; [R3,xp] = radon(P,theta3); >> figure, imagesc(theta3,xp,R3); colormap(hot); colorbar xlabel('heta'); ylabel('xrime'); >> I1 = iradon(R1,10); I2 = iradon(R2,5); I3 = iradon(R3,2); imshow(I1); figure, imshow(I2); figure, imshow(I3);

20. Inverse Radon Transform

21. Marker-Controlled Watershed Segmentation Separating touching objects in an image is one of the more difficult image processing operations. The watershed transform is often applied to this problem.

23. Step 2: Use the Gradient Magnitude as the Segmentation Function

24. Step 3: Mark the Foreground Objects

25. Step 4: Compute Background Markers

26. Step 5: Compute the Watershed Transform of the Segmentation Function.